Скачать или смотреть How to Normalize a NumPy Array with Custom Min and Max Values for Each Column

Learn how to effectively normalize a NumPy array by presetting your own min and max values for each column. This step-by-step guide will help you implement this functionality using Python.
---
This video is based on the question https://stackoverflow.com/q/67548805/ asked by the user 'user' ( https://stackoverflow.com/u/15580285/ ) and on the answer https://stackoverflow.com/a/67574811/ provided by the user 'user' ( https://stackoverflow.com/u/15580285/ ) at 'Stack Overflow' website. Thanks to these great users and Stackexchange community for their contributions.

Visit these links for original content and any more details, such as alternate solutions, latest updates/developments on topic, comments, revision history etc. For example, the original title of the Question was: Normalize numpy array to min max different form column min max

Also, Content (except music) licensed under CC BY-SA https://meta.stackexchange.com/help/l...
The original Question post is licensed under the 'CC BY-SA 4.0' ( https://creativecommons.org/licenses/... ) license, and the original Answer post is licensed under the 'CC BY-SA 4.0' ( https://creativecommons.org/licenses/... ) license.

If anything seems off to you, please feel free to write me at vlogize [AT] gmail [DOT] com.
---
How to Normalize a NumPy Array with Custom Min and Max Values for Each Column

When working with data in machine learning and data analysis, normalization is a crucial step. It scales the data to a specified range, which can improve the performance and training speed of models. However, there are situations where you may want to use custom minimum and maximum values for normalization. This guide will guide you through how to normalize a NumPy array while allowing preset min and max values for each column, rather than relying purely on the minimum and maximum values of the data itself.

Identifying the Problem

You may have a NumPy array derived from a dataset that includes multiple columns, each representing different features. For certain applications, directly normalizing against the column's inherent min and max values isn’t sufficient.

Example:

For instance, if you have the following column specifications:

wave1: min=100, max=300

value2: min=2, max=10

value3: min=10, max=100

You need to normalize these columns with these specific ranges, instead of using the minimum and maximum values from the data in those columns.

Proposed Solution

To achieve this, you can follow the process outlined below. Essentially, you will:

Define your preset min-max values.

Add these values to your NumPy array.

Normalize the array using your custom function.

Remove the min-max rows after normalization.

Step-by-Step Implementation

1. Define Preset Min-Max Values

You will need to prepare a separate file or an array to hold your custom minimum and maximum values for each column. In this example, we will read them from an Excel file.

[[See Video to Reveal this Text or Code Snippet]]

2. Create the Normalization Function

The normalization function will utilize the preset values:

[[See Video to Reveal this Text or Code Snippet]]

3. Check for Value Errors

Add a function to verify if the data is within your specified limits:

[[See Video to Reveal this Text or Code Snippet]]

4. Normalize the Data

Finally, integrate everything into a main normalization function:

[[See Video to Reveal this Text or Code Snippet]]

Conclusion

This method allows you to effectively normalize a NumPy array using custom minimum and maximum values for each column. By embedding the min-max checks and creating a clear structure for normalization, your data will be correctly scaled while adhering to the necessary specifications. This approach is particularly useful in scenarios where domain-specific knowledge dictates the bounds of your data.

Implement this solution in your projects to ensure more flexibility and accuracy in your data preprocessing steps!